def get_df(cols, load_from_temp, temp_path):
weather_df = load_weather_df(CONFIG.preprocessed_meteo_path_complete)
if not load_from_temp:
logger.info('Loading train Dataframe...')
train_df = load_train_df(CONFIG.preprocessed_train_path_means)
logger.info('Loading weather Dataframe...')
logger.info('Creating features...')
ff = FeatureFactory(train_df, weather_df)
for col in cols:
logger.info('Creating %s feature...' % col)
ff(col)
if 'ASS_ASSIGNMENT' not in cols:
cols = ['ASS_ASSIGNMENT'] + cols
if 'DATE' not in cols:
cols = ['DATE'] + cols
if 'CSPL_RECEIVED_CALLS' not in cols:
cols += ['CSPL_RECEIVED_CALLS']
logger.info('Selecting features...')
ff.select_features(cols)
train_df = ff.X
if temp_path is not None:
train_df.to_csv(temp_path)
else:
assert temp_path is not None
logger.info('Loading train Dataframe...')
train_df = pd.read_csv(temp_path, encoding='latin-1', index_col=0, parse_dates=['DATE'])
weather_df.reset_index(inplace=True)
return train_df, weather_df
def complete_meteo_with_zeros(in_path, out_path=None):
logger.debug('Loading Dataframe...')
weather_df = load_weather_df(in_path)
means = weather_df.mean()
weather_df.reset_index(inplace=True)
logger.debug('Generating empty Dataframe...')
d1 = date(2011, 1, 1)
d2 = date(2012, 12, 31)
dates = [d1]
while d1 < d2:
d1 += td(days=1)
dates.append(d1)
zero_df = pd.DataFrame({'DATE': dates}, dtype=weather_df.dtypes['DATE'])
logger.debug('Completing Dataframe...')
weather_df = zero_df.merge(weather_df.copy(), how='left', on='DATE')
weather_df['NUMB_FROZEN_DEPT'].fillna(means['NUMB_FROZEN_DEPT'], inplace=True)
weather_df['NUMB_WET_DEPT'].fillna(means['NUMB_WET_DEPT'], inplace=True)
weather_df.set_index('DATE', inplace=True)
logger.debug('Saving Dataframe...')
if out_path is not None:
weather_df.to_csv(out_path)
return weather_df
def compare_calls(scale, out_path, assignments=None, datetime=None):
"""
Plot the number of calls to compare them.
Parameters
==========
scale: 'DATETIME', 'DAY', 'WEEK' or 'YEAR', calls are averaged on all smaller scales,
and plotted for larger scales.
out_path: str, folder in which figures should be saved.
assignments: str or list of str, assignments to take into account.
None to take all columns into account.
datetime: if 'DATETIME', the datetime to filter on
Example
=======
Week comparison: For each day of the week, take the average number of calls, then compare for each week of the year.
"""
assert scale in ['DATETIME', 'DAY', 'WEEK', 'YEAR']
if assignments is not None:
if isinstance(assignments, str):
assignments = [assignments]
assert not set(assignments).difference(CONFIG.submission_assignments)
else:
assignments = CONFIG.submission_assignments
df = load_train_df(CONFIG.preprocessed_train_path)
df = df[df["ASS_ASSIGNMENT"].isin(assignments)]
# if remove_days_off:
# df = df[df["DAY_OFF"] == 0]
# df.drop("DAY_OFF", axis=1, inplace=True)
ff = FeatureFactory(df)
for column in ["WEEK_NUMBER", "WEEK_DAY", "TIME"]:
ff(column)
df = ff.X
if scale == 'DATETIME':
assert datetime is not None
df = df[ff("WEEK_DAY") == datetime.isoweekday()]
df = df[df['TIME'] == datetime.hour + float(datetime.minute)/60]
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment].reset_index()
plt.plot(df_assignment['CSPL_RECEIVED_CALLS'])
weather_df = load_weather_df(CONFIG.preprocessed_meteo_path)
good_days = [d for d in weather_df.index if d.isoweekday() == datetime.isoweekday()]
weather_df = weather_df.loc[good_days, :].reset_index()
plt.plot(weather_df['NUMB_FROZEN_DEPT'])
plt.plot(weather_df['NUMB_WET_DEPT'])
plt.savefig(os.path.join(out_path, scale+".jpg"))
if scale == 'DAY':
grouped = df.groupby(["ASS_ASSIGNMENT", "WEEK_NUMBER", "WEEK_DAY", "TIME"])
df = grouped["CSPL_RECEIVED_CALLS"].sum().reset_index()
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment]
for day in range(366):
df_day = df_assignment[df_assignment['WEEK_NUMBER'] == int(day/7 + 1)]
df_day = df_day[df_day['WEEK_DAY'] == day % 7]
plt.plot(df_day['TIME'], df_day["CSPL_RECEIVED_CALLS"])
plt.savefig(os.path.join(out_path, scale+"_"+assignment+".jpg"))
plt.clf()
if scale == 'WEEK':
grouped = df.groupby(["ASS_ASSIGNMENT", "WEEK_NUMBER", "WEEK_DAY"])
df = grouped["CSPL_RECEIVED_CALLS"].mean().reset_index()
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment]
for week_number in range(53):
df_week = df_assignment[df_assignment['WEEK_NUMBER'] == week_number]
plt.plot(df_week['WEEK_DAY'], df_week["CSPL_RECEIVED_CALLS"])
plt.savefig(os.path.join(out_path, scale+"_"+assignment+".jpg"))
plt.clf()
if scale == 'YEAR':
grouped = df.groupby(["ASS_ASSIGNMENT", "WEEK_NUMBER"])
df = grouped["CSPL_RECEIVED_CALLS"].mean().reset_index()
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment]
plt.plot(df_assignment['WEEK_DAY'], df_assignment["CSPL_RECEIVED_CALLS"])
# plt.axis([0, 52, 0, 50])
plt.savefig(os.path.join(out_path, scale+"_absolute_values.jpg"))
plt.clf()
